Erik Herrmann
Saarland University
15 Papers
48 Citations
Erik Herrmann is an academic researcher from Saarland University. The author has contributed to research in topics: Motion capture & Motion (physics). The author has an hindex of 6, co-authored 15 publications. Previous affiliations of Erik Herrmann include German Research Centre for Artificial Intelligence.
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Papers
Stylistic Locomotion Modeling and Synthesis using Variational Generative Models
Han Du,Erik Herrmann,Janis Sprenger,Klaus Fischer,Philipp Slusallek +4 more
- 28 Oct 2019
TL;DR: This work proposes a variational generative model to combine the large variation in neutral motion database and style information from a limited number of examples to create generative models for distinctive styles of locomotion for humanoid characters.
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Agent-based Web Supported Simulation of Human-robot Collaboration
André Antakli,Torsten Spieldenner,Dmitri Rubinstein,Daniel Spieldenner,Erik Herrmann,Janis Sprenger,Ingo Zinnikus +6 more
- 18 Sep 2019
TL;DR: A framework for 3D simulation of hybrid teams in production scenarios based on an agent framework that can be used to evaluate critical properties of the planned production environment and the dynamic assignment of tasks to team members is presented.
13
Scaled functional principal component analysis for human motion synthesis
Han Du,Somayeh Hosseini,Martin Manns,Erik Herrmann,Klaus Fischer +4 more
- 10 Oct 2016
TL;DR: This work proposes a novel method called Scaled Functional Principal Component Analysis (SFPCA) that is able to scale the features of motion data for FPCA through a general optimization framework that can automatically adapt to different parameterizations of motion.
12
Joint Angle Data Representation for Data Driven Human Motion Synthesis
TL;DR: Different ways of representing joint angles from motion capture data are explored: Euler angle, quaternion and exponential map, which shows best performance for motion data representation, which contradicts a preference in literature for exponential map representation.
12
Accelerating statistical human motion synthesis using space partitioning data structures
TL;DR: This paper fits a statistical motion model to the low‐dimensional latent space obtained by applying principal component analysis to a functional representation of example motion data to accelerate the synthesis of statistical model‐based motion synthesis.
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